In the mapping community there is a growing need for accurate digital elevation data. Such data are frequently used to calculate slope, aspect, and intervisibility. In the area of remote sensing, digital elevation data may be used to generate perspective views for flight simulators, 3-D mapping, or for orthorectification of digital satellite data. Orthorectification is a process of removing the horizontal displacement, and thus knowledge of the elevation is important in accurate determination of correct features location.
It is well known to prepare maps from stereo images obtained from satellite or aircraft overflights. Features in the images are horizontally displaced due to viewing geometry and terrain relief. The displacement due to relief is a function of the look angle and the elevation. Heretofore, the extraction of elevation data from stereo images involved optical techniques requiring intensive human effort or computer operations assisted by human interaction. Some techniques involve correlation and regression. One of the major drawbacks to the correlation approach is the intensive computer processing that must be done.
A feature matching approach is much less computer-intensive. An additional advantage is that it is thought to best imitate the human vision system. Since human vision has the ability to quickly judge distances, it serves as a good model for a machine-based approach. It is postulated in Marr, D., 1982, Vision, A Computational Investigation into the Human Representation and Processing of Visual Information, W. H. Freeman and Co., San Francisco, that human vision rapidly identifies features in a scene by applying a large bandpass filter to the image to form a general impression followed by a series of successively smaller filters for more detail. Computer simulation of vision is described, and Marr suggests that the ideal filter is Laplacian, although the simulation used a nested series of filters which approximates Laplacian filters. The simulation used feature extraction by identifying zero crossings in the filtered images.